Recent advances in radiation therapy [1], such as Intensity Modulated Radiotherapy (IMRT) and Image-Guided Radiotherapy (IGRT), offer the ability to maximize tumor control while reducing the risk of radiation-induced damage to adjacent normal tissue. Typically, radiation therapy involves three phases: (1) prescription - where radiation oncologists (physicians) specify the dose constraints for targets and organs at risk (OAR); (2) planning - where treatment planners (physicists, dosimetrists) determine the treatment parameters to achieve the prescribed dose constraints; and (3) treatment - where therapists carry out the plan to treat the patients. In current practice, radiation oncologists typically draw on a variety of sources for dose prescription, including the 1991 Emami paper [8] on normal tissue tolerance, updated guidance from QUANTEC, other data in journals and texts, and their personal experiences. While these provide a general understanding of the dependence of normal tissue complication on dose distribution or the upper limits of the organ tolerance in populations of patients, their application to an individual patient is less certain and precise. Application of data and guidelines that are available in the literature is further complicated by the fact that this information is available only as narrative texts, tables and charts that are difficult to quantitatively integrate into clinical practice. Furthermore, the existing guidelines do not consider patient specific information regarding the ideal dose distribution achievable at individual treatments [9]. Radiation oncologists are frequently forced to make difficult prescription decisions by synthesizing available population level guidelines, personal experience, and their understanding of the specific patient needs on an ad hoc basis. Our overarching goal is to improve outcome by providing evidence-based decision support for radiation oncologists, planners, and therapists in every phase of the treatment process. In this project we propose to develop practical and clinically useful decision support tools to help radiation oncologists prescribe patient- specific optimal dose constraints. The specific aims are (1) Provide radiation oncologists with reliable predictions of patient-specific dose distributions achievable for the patient's anatomy and tumor volume; and (2) Provide radiation oncologists with intuitive tools that integrate patient-specific dose predictions with population-based dose guidelines to support prescription decision making. We believe the technologies developed in this project will not only improve the quality of radiotherapy prescriptions but also reduce planning time with optimal dose constraints and improve clinical outcomes.